Multiple Measurement Vector Based Complex-Valued Multi-Frequency ECT

omplex-Valued, Multi-Frequency Electrical Capacitance Tomography (CVMF-ECT) is a recently developed tomographic concept which is capable to simultaneously reconstruct spectral permittivity and conductivity properties of target objects within the region of interest. To date, this concept has been limited to simulation and another key issue restricting its wide adoption lies in its poor image quality. This paper reports a CVMF-ECT system to verify its practical feasibility and further proposes a novel image reconstruction framework to effectively and efficiently reconstruct multi-frequency images using complex-valued capacitance data. The image reconstruction framework utilizes the inherent spatial correlations of the multi-frequency images as a priori information and encodes it by using Multiple Measurement Vector (MMV) model. Alternating direction method of multipliers was introduced to solve the MMV problem. Real-world experiments validate the feasibility of CVMF-ECT, and MMV based CVMF-ECT method demonstrates superior performance compared to conventional ECT approaches

Multiphase flow measurement and data analytic based on multi-modal sensors

Accurate multiphase flow measurement is crucial in the energy industry. Over the past decades, separation of the multiphase flow into single-phase flows has been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase flows remain a long-standing challenge. To tackle the challenge, this thesis first explores the feasibility of performing time-difference and frequency-difference imaging of multiphase flows with complex-valued electrical capacitance tomography (CVECT). The multiple measurement vector (MMV) model-based CVECT imaging algorithm is proposed to reconstruct conductivity and permittivity distribution simultaneously, and the alternating direction method of multipliers (ADMM) is applied to solve the multi-frequency image reconstruction problem. The proposed multiphase flow imaging approach is verified and benchmarked with widely adopted tomographic image reconstruction algorithms. Another focus of this thesis is multiphase flowrate estimation based on low-cost, multi-modal sensors. Machine learning (ML) has recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods, i.e., deep neural network (DNN), support vector machine (SVM), and convolutional neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network (TCN) model is introduced to analyse the collected time series sensing data from the Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built to predict the multiphase flowrate with various data pre-processing methods. The results provide guidance on data pre-processing methods for multiphase flowrate measurement and suggest that the proposed combination of low-cost flow sensing techniques and machine learning can effectively translate the time series sensing data to achieve satisfactory flowrate measurement under various flow conditions

Compressive Sensing Theory for Optical Systems Described by a Continuous Model

A brief survey of the author and collaborators' work in compressive sensing applications to continuous imaging models.Comment: Chapter 3 of "Optical Compressive Imaging" edited by Adrian Stern published by Taylor & Francis 201

Macroscopic equations governing noisy spiking neuronal populations

At functional scales, cortical behavior results from the complex interplay of a large number of excitable cells operating in noisy environments. Such systems resist to mathematical analysis, and computational neurosciences have largely relied on heuristic partial (and partially justified) macroscopic models, which successfully reproduced a number of relevant phenomena. The relationship between these macroscopic models and the spiking noisy dynamics of the underlying cells has since then been a great endeavor. Based on recent mean-field reductions for such spiking neurons, we present here {a principled reduction of large biologically plausible neuronal networks to firing-rate models, providing a rigorous} relationship between the macroscopic activity of populations of spiking neurons and popular macroscopic models, under a few assumptions (mainly linearity of the synapses). {The reduced model we derive consists of simple, low-dimensional ordinary differential equations with} parameters and {nonlinearities derived from} the underlying properties of the cells, and in particular the noise level. {These simple reduced models are shown to reproduce accurately the dynamics of large networks in numerical simulations}. Appropriate parameters and functions are made available {online} for different models of neurons: McKean, Fitzhugh-Nagumo and Hodgkin-Huxley models

Regional admittivity reconstruction with multi-frequency complex admittance data using contactless capacitive electrical tomography

Tomographic imaging of the electrical properties distribution within biological subjects such as the human body has been an active research goal in electrical tomography (ET). As the electrical properties of a living tissue vary with the excitation frequency, measuring the frequency-dependent behaviour of the effective dielectric can increase the possibilities for tissue characterisation, and thus enhance the potential for extended clinical applications. The ET system generally enables to capture the changes in effective dielectric properties at low spatial resolution, therefore, the complete complex admittance spectrum can be reconstructed by ET to enrich the information content and further provide better diagnostic. In this work, we demonstrate a novel contactless ET system which relies on the capacitive coupled principle, the capacitive coupled electrical tomography (CCET). Except the non-contact measuring characteristic, the capacitance-based imaging principle enables the system to obtain the measurements at higher excitation frequencies. These characteristics give CCET great potential in future medical application, as the high-frequency component of complex impedance plays a dominant role in establishing the link between the microscopic cell structures and the macroscopic admittivity images obtained from multi-frequency ET systems. In this paper, we used multi-frequency electrical signals from 320 kHz to 14 MHz to conduct the single and multiple inclusions test with different biological samples. Both the reconstructed tomographic images and the Cole-Cole plots confirm the ability of CCET in characterising different objects.</p